manufacture
Forrester says that 58% of professionals are researching AI systems, yet only 12% are actively using them in manufacturing.

AI in manufacturing

Artificial Intelligence (AI) promises to be the next breakthrough in improving productivity in the manufacturing industry. It has the potential to enhance and extend human capabilities, and help manufacturing businesses achieve more. Driven by AI, analytics and real-time insights help manufacturing businesses grow their revenues and market shares faster than their peers in industries such as ecommerce and healthcare.

The Annual Manufacturing Report found that nearly 92% of senior executives in the manufacturing industry expect that ‘Smart Factory’ digital technologies – including Artificial Intelligence – will empower them to improve productivity levels and enable their staff to work smarter. Similarly, Forrester says that 58% of business and technology professionals are researching AI systems, yet only 12% are actively using them.

But is productivity the only benefit that artificial intelligence can deliver?

Disruptions affecting the manufacturing industry

When AI is applied to manufacturing processes, it requires certain foundational technologies to be in place. For instance, a smart factory needs to be networked by taking data from production lines, engineering and design teams, and quality control in order to form a well-integrated and intelligence operation.

But if manufacturers don’t’ have these smart machines or the right data, it ends up becoming only data with no insights. We know that insight is what optimizes operations. So, first manufacturing companies need to become digital companies if they want to retain sales, revenue, and customers. It’ll need digital transformation.

Linear customer journeys are the stuff of the past. Today, you need to create a 360- degree view of the customer. You should be able to bring the product to the center of the operations. You need to understand how the product is used. This brings huge benefits from new product development through to improved go to market models. Each of these will add data collection points for AI and cognitive services to advance the operation and ultimately the customer experience.

Predictive maintenance

Maintenance plays a key role in any manufacturing operation’s expenses. So, predictive maintenance is now a common goal amongst manufacturers who are drawn by its many benefits. One of the most compelling benefits is the significant cuts in maintenance costs.

While certain manufacturers do perform predictive maintenance, this has traditionally been done using SCADA systems set up with human-coded thresholds, alert rules and configurations. The problem with such an arrangement is two-fold. One is the cost and time involved in ensuring optimal results. Two, this traditional process does not consider the more complex dynamic behavioral patterns of the machinery, or the contextual data relating to the manufacturing process
at large.

Let’s take for example, a sensor on a production machine. The sensor may pick up a sudden temperature rise. A static rule-based system would not take into account the fact that the machine is undergoing sterilization, and would proceed to trigger a false-positive alert.

However, with the help of machine learning (ML), the algorithms are fed OT data (from the production floor: sensors, PLCs, historians, SCADA), IT data (contextual data: ERP, quality, MES, etc.), and manufacturing process information describing the synchronicity between the machines and the rate of production flow.

The AI models are trained on all of this data. The training enables the models or algorithms to detect anomalies in the machine. It also tests correlations while looking for patterns across various data feeds.

Additionally, the power of AI and machine learning lies in its capacity to analyze very large amounts of data quickly and suggest actionable insights and responses in real-time. This way, the health and behavior of every asset and system are continuously evaluated and monitored. Every component deterioration is identified ahead of the machine’s malfunction.

Predictive Quality Analytics

Industrial AI can not only assist manufacturing companies with preventing downtime but also ensure that the quality of output is top notch by product quality deterioration. By knowing ahead that the quality of manufactured products can be expected to drop, prevents the wastage of raw materials and valuable production time.

Other benefits of AI and ML

AI and ML present an ocean of change that comes packed with many benefits. These can be advantageous beyond improving productivity and bringing new business. Here are some of the direct benefits of AI and ML in manufacturing include:

  • Reduce cost with predictive maintenance. This of course leads to reduced maintenance activity. This translates to lower labor costs and reduced inventory and materials wastage.
  • Predicting RUL is short for remaining useful life. By learning more about the behavior of machines and equipment you will be able to create conditions that improve performance while maintaining machine health. With the help of predicting RUL you can do away with unpleasant and unplanned downtime.
  • When you are able to manage your inventory efficiently and ensure a well-monitored and synchronized production flow, you can witness improved supply chain management.
  • AI also helps you improved quality control with the help of actionable insights that enable you to raise product quality.
  • Consumer-focused manufacturing – being able to respond quickly to changes in the market demand.

If you are looking for an AI solution to help you scale up your efforts in your manufacturing company, leave us a comment or write to us at contact@brainayzed.com

Subscribe to our newsletter

AI whitepaper download

Want to use AI to enhance portfolio management offerings?

Related articles

AI platform for the world’s
data-driven companies